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Probabilistic Risk Assessment of the Environmental Impacts of Pesticides in the Crocodile (West) Marico Catchment, North-West Province

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Probabilistic Risk Assessment of the Environmental Impacts of Pesticides in the Crocodile (West) Marico Catchment, North-West Province Probabilistic risk assessment of the environmental impacts of pesticides in the Crocodile (west) Marico catchment, North-West Province TM Ansara-Ross1*, V Wepener1, PJ Van den Brink2, 3 and MJ Ross1 1 Centre for Aquatic Research, Department of Zoology, University of Johannesburg, Auckland Park, PO Box 524, Johannesburg 2006, South Africa 2Wageningen University, Department of Aquatic Ecology and Water Quality Management, Wageningen University and Research centre, PO Box 47, 6700 AA Wageningen, The Netherlands 3Alterra, Wageningen University and Research centre, PO Box 47, 6700 AA Wageningen, The Netherlands Abstract External agricultural inputs, such as pesticides, may pose risks to aquatic ecosystems and affect aquatic populations, commu- nities and ecosystems. To predict these risks, a tiered approach was followed, incorporating both the PRIMET and PERPEST models. The first-tier PRIMET model is designed to yield a relatively worst-case risk assessment requiring a minimum of input data, after which the effects of the risks can be refined using a higher tier PERPEST model. The risk assessment initially depends on data supplied from local landowners, pesticide characteristic, application scheme and physical scenario of the environment under question. Preliminary results are presented, together with ecotoxicological data on several frequently-used pesticides in a section of the Crocodile (west) Marico Water Management Area (WMA) in South Africa. This area is historically known to have a high pesticide usage, with deltamethrin, aldicarb, parathion, cypermethrin and dichlorvos being the main pesticides used. Deltamethrin was indicated as having the highest probability of risks to aquatic organisms occurring in the study area. Cypermethrin, parathion, dichlorvos, carbaryl, bromoxynil, linuron, methomyl and aldicarb were all indicated as having pos- sible risks (ETR 1-100) to the aquatic environment. Pesticides posing no risk included fenamiphos, abamectin, pendimethalin, captan, endosulfan, alachlor, bentazone and cyromazine (ETR<1). The pesticides posing a possible risk to the aquatic ecosystem were evaluated further to determine their effects on 8 grouped endpoints using the PERPEST effect model. Deltamethrin and cypermethrin were again noted as posing the greatest risk and clear effects were eminent for aquatic insects and macro-crusta- ceans, followed by micro-crustaceans and rotifers. High percentages of clear effects on insects were also observed for carbaryl, parathion and dichlorvos. Linuron was indicated as having minimal clear effects on community metabolism, macrophytes and phytoplankton classes, while lesser clear effects of bromoxynil occurred on periphyton communities. Application of both the lower-tier PRIMET and higher-tier PERPEST models showed similar trends in that they both ranked the top 5 pesticides in the same order of risk. This approach offers a significant improvement over the presently-used simulation models or use of safety factors. It is therefore especially useful in developing countries such as South Africa, where pesticide environmental risk information is scarce. Although these models were effectively used in this study, it still has to be validated further under South African conditions Keywords: risk-assessment model, pesticides, aquatic ecosystem Introduction (e.g. Schulz (2001) and Bollmohr et al. (2007)) focus primarily on pesticide exposures and effects in estuaries and rivers within Agriculture forms an important component of South Africa’s the Western Cape. These authors, however, do not report on the economy, with extrinsic inputs, like pesticides and fertilisers, application of a probabilistic modelling approach for pesticide helping to ensure a reliable and high agricultural yield. The use of risk assessment for South Africa. such agrochemicals has benefited the quality of human health in Present techniques used in assessing the ecological fate terms of food security and quality. Extensive and improper appli- and effect of pesticides under realistic field conditions involve cation of these pesticides, however, may pose risks to aquatic eco- a large number of parameters to be measured. These can there- systems and consequently affect non-target populations, commu- fore incorporate intricate and detailed simulation models such nities and ecosystems. A large number of different pesticides are as ecological, population or food-web models (e.g. Koelmans et currently available to agriculture, most of which are applied fre- al., 2001; Traas et al., 1998; Van den Brink et al., 2007). These quently. For this reason, it is important to be able to assess which risk-assessment models often have drawbacks, which include of these chemicals pose the greatest risks to the environment. a lack of transparency, a high degree of complexity that can As far as could be ascertained, no risk-assessment studies cause compounding errors through erroneous primary data, and using probabilistic models have been undertaken within South expensive implementation thereof (Van den Brink et al., 2006; Africa to determine the effects of pesticides to the freshwater Van der Werf, 1996). The intricate input data required are also aquatic environment. The studies that have been undertaken unavailable for many pesticides. These models also very often focus only on certain aspects of risk assessment and there- * To whom all correspondence should be addressed. fore have limited application. These aspects pose restrictions, +2711 467 2931; fax: +2711 467 2931; especially for developing countries that lack the resources (Lon- e-mail: [email protected] don et al., 2005). Available on website http://www.wrc.org.za 637 ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) The development of the PRIMET and PERPEST models was field experiments and includes actual case scenarios, whilst the aimed at surpassing these limitations by being centred on deliver- SSD concept is based on laboratory data only. ing results of increased scope of application, with limited input The PERPEST model (Van den Brink et al., 2002; Van Nes data being required to evaluate the risks of pesticides to the envi- and Van den Brink, 2003; Van den Brink et al., 2006) predicts the ronment. This allows for people without specialist training in the toxic effects of a particular concentration of a pesticide on grouped scientific field to utilise the models for monitoring as well as for endpoints. The PERPEST expert model is based on a case-based management applications (e.g. farmers). reasoning (CBR) approach. This is a technique that solves new The risks of pesticides to the environment are evaluated by problems by using past experience. For developing the model, taking into account factors associated with both exposure (by cal- empirical data were extracted from published literature describ- culating the predicted concentrations) and effect assessments (by ing the results from mesocosm and microcosm experiments for determining safe concentrations based on laboratory toxicity data freshwater model ecosystem studies with pesticides (Brock et al., and the use of assessment factors) of the compound in question 2000a; 2000b) and were collated within a database. When evalu- and incorporating the quantity used and doses applied to a defined ating the effects of insecticides, the results of these experiments scenario. Application rate is known to be an important explana- are assigned to 1 of 8 endpoint categories: algae and macrophytes; tory variable determining the concentration of the pesticide in the community metabolism; fish; insects; macro-crustaceans; micro- environment (Van der Werf, 1996; Tesfamichael and Kaluarach- crustaceans; other invertebrates and rotifers. For herbicides, chi, 2006) and needs to be taken into consideration when using the 8 effect groupings are as follows: macrophytes; periphyton; risk-assessment models. This aspect is taken into consideration phytoplankton; zooplankton; community metabolism; fish and for these models. tadpoles; molluscs and macro-crustaceans and insects (Van den The PRIMET Decision Support System (Pesticide Risks in Brink et al., 2006). Except for community metabolism, all other the Tropics to Man, Environment and Trade, Van den Brink et al., effect categories represent structural endpoints, while the former 2005) can be applied to estimate the lower-tier risks of pesticide represents a functional response. When the effect of a particu- application on aquatic and terrestrial ecosystems, groundwater as lar concentration of a particular pesticide has to be predicted, the well as human health aspects - via dietary exposure by consuming PERPEST model searches for analogous situations in the data- vegetables, fish or macrophytes. In this preliminary risk assess- base based on relevant (toxicity) characteristics of the compound, ment only entry via spray drift was taken into consideration. exposure concentration and type of ecosystem to be evaluated This model uses defined scenarios and is applicable to a warmer (Van den Brink et al., 2006). This allows the model to use infor- environment, specifically in developing countries (such as South mation on other pesticides when predicting effects of a particular Africa) as it includes a temperature-dependent assessment of pesticide. The parameters for the prediction can be optimised by the exposure concentration (this is included in the degradation using a controlled random search procedure (Van den Brink et and volatilisation rate parameters
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